{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "OpenHand-Models.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.9"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArthurFDLR/pose-classification-kit/blob/master/examples/hand_pose_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMvrR9_jnDwO"
      },
      "source": [
        "# 🤙 Pose Classification Kit: Hand pose classification model creation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONjOBMTanXiN"
      },
      "source": [
        "This Notebook can be used to create Neural Network classifiers running in the [Pose Classification Kit](https://github.com/ArthurFDLR/pose-classification-kit).\n",
        "\n",
        "First, we have to import several libraries to create and train a new model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Xvt8oHFae-I",
        "outputId": "c1ec4481-2f9f-44f2-a41f-c1076343b6db"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "from matplotlib.lines import Line2D\n",
        "import os\n",
        "\n",
        "try:\n",
        "    import google.colab\n",
        "    IN_COLAB = True\n",
        "except:\n",
        "    IN_COLAB = False\n",
        "\n",
        "if IN_COLAB:\n",
        "    %tensorflow_version 2.x\n",
        "    !pip install pose-classification-kit\n",
        "    \n",
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from pose_classification_kit.datasets import handDataset\n",
        "\n",
        "print('Available GPU:')\n",
        "!nvidia-smi -L\n",
        "print('\\nTensorFlow use GPU at: {}'.format(tensorflow.test.gpu_device_name()))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pose-classification-kit\n",
            "  Downloading pose_classification_kit-1.1.5-py3-none-any.whl (24.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 24.2 MB 24 kB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas<2.0.0,>=1.1.5 in /usr/local/lib/python3.7/dist-packages (from pose-classification-kit) (1.1.5)\n",
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            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (from pose-classification-kit) (2.5.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0.0,>=1.1.5->pose-classification-kit) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0.0,>=1.1.5->pose-classification-kit) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas<2.0.0,>=1.1.5->pose-classification-kit) (1.15.0)\n",
            "Requirement already satisfied: grpcio~=1.34.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->pose-classification-kit) (1.34.1)\n",
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            "Requirement already satisfied: keras-nightly~=2.5.0.dev in /usr/local/lib/python3.7/dist-packages (from tensorflow->pose-classification-kit) (2.5.0.dev2021032900)\n",
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            "Requirement already satisfied: gast==0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->pose-classification-kit) (0.4.0)\n",
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            "Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow->pose-classification-kit) (0.2.0)\n",
            "Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow->pose-classification-kit) (1.1.2)\n",
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            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow->pose-classification-kit) (2.23.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow->pose-classification-kit) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow->pose-classification-kit) (4.7.2)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow->pose-classification-kit) (4.2.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow->pose-classification-kit) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.5->tensorflow->pose-classification-kit) (4.6.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow->pose-classification-kit) (0.4.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow->pose-classification-kit) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow->pose-classification-kit) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow->pose-classification-kit) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow->pose-classification-kit) (2021.5.30)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow->pose-classification-kit) (3.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.5->tensorflow->pose-classification-kit) (3.5.0)\n",
            "Installing collected packages: pose-classification-kit\n",
            "Successfully installed pose-classification-kit-1.1.5\n",
            "1 Physical GPUs, 1 Logical GPUs\n",
            "Available GPU:\n",
            "GPU 0: Tesla T4 (UUID: GPU-af7c2a09-6b1f-1f69-1e09-3f5cce81eb8e)\n",
            "\n",
            "TensorFlow use GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSmWOsSRg8i5"
      },
      "source": [
        "## Import dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_v5VYGMCg8i6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8ff7a93-970f-4b65-c3c8-05e2da9b825f"
      },
      "source": [
        "dataset = handDataset(testSplit=.2, shuffle=True, handID=1)\n",
        "x_train = dataset['x_train']\n",
        "y_train = dataset['y_train_onehot']\n",
        "labels = dataset['labels']\n",
        "\n",
        "x_train.shape, y_train.shape"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4347, 42), (4347, 27))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xRWuVIAHae-P"
      },
      "source": [
        "## Models exploration\n",
        "\n",
        "This section is optional. The following blocks can be used to compare different model architecture and training processes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93-aeO7Sae-P"
      },
      "source": [
        "model_train_history = {}\n",
        "input_dim = x_train.shape[1]\n",
        "validation_split = 0.20\n",
        "epochs = 15"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZ2p8pTDae-P",
        "outputId": "61465c76-065d-4e51-babc-596eb077b521"
      },
      "source": [
        "model = keras.models.Sequential(name = 'ANN-3x16',\n",
        "    layers =\n",
        "    [\n",
        "        keras.layers.InputLayer(input_shape=input_dim),\n",
        "        keras.layers.Dense(16, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(16, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(16, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(len(labels), activation=keras.activations.softmax),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(),\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "model_train_history[model] = model.fit(\n",
        "    x=x_train,\n",
        "    y=y_train,\n",
        "    epochs=epochs,\n",
        "    batch_size=4,\n",
        "    validation_split=validation_split,\n",
        "    shuffle=True,\n",
        "    verbose=1,\n",
        ")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"ANN-3x16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 16)                688       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 27)                459       \n",
            "=================================================================\n",
            "Total params: 1,691\n",
            "Trainable params: 1,691\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "870/870 [==============================] - 6s 3ms/step - loss: 2.1664 - accuracy: 0.3739 - val_loss: 0.9488 - val_accuracy: 0.7379\n",
            "Epoch 2/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.5192 - accuracy: 0.9025 - val_loss: 0.2680 - val_accuracy: 0.9736\n",
            "Epoch 3/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.1699 - accuracy: 0.9753 - val_loss: 0.1430 - val_accuracy: 0.9770\n",
            "Epoch 4/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0790 - accuracy: 0.9885 - val_loss: 0.0575 - val_accuracy: 0.9920\n",
            "Epoch 5/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0429 - accuracy: 0.9934 - val_loss: 0.0399 - val_accuracy: 0.9931\n",
            "Epoch 6/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0268 - accuracy: 0.9963 - val_loss: 0.0209 - val_accuracy: 0.9989\n",
            "Epoch 7/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0246 - accuracy: 0.9951 - val_loss: 0.0188 - val_accuracy: 0.9966\n",
            "Epoch 8/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0180 - accuracy: 0.9957 - val_loss: 0.0158 - val_accuracy: 0.9966\n",
            "Epoch 9/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0162 - accuracy: 0.9957 - val_loss: 0.0136 - val_accuracy: 0.9966\n",
            "Epoch 10/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0113 - accuracy: 0.9974 - val_loss: 0.0089 - val_accuracy: 0.9989\n",
            "Epoch 11/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0104 - accuracy: 0.9974 - val_loss: 0.0082 - val_accuracy: 0.9989\n",
            "Epoch 12/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0119 - accuracy: 0.9963 - val_loss: 0.0105 - val_accuracy: 0.9966\n",
            "Epoch 13/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.0040 - val_accuracy: 0.9989\n",
            "Epoch 14/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0052 - accuracy: 0.9994 - val_loss: 0.0060 - val_accuracy: 0.9989\n",
            "Epoch 15/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0054 - accuracy: 0.9980 - val_loss: 0.0043 - val_accuracy: 0.9989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ils0Rcv4ae-Q",
        "outputId": "2cf2db86-5a2a-4677-bcce-23771463ce89"
      },
      "source": [
        "model = keras.models.Sequential(name = 'ANN-3x64',\n",
        "                                   layers =\n",
        "    [\n",
        "        keras.layers.InputLayer(input_shape=input_dim),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(len(labels), activation=keras.activations.softmax),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(),\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "model_train_history[model] = model.fit(\n",
        "    x=x_train,\n",
        "    y=y_train,\n",
        "    epochs=epochs,\n",
        "    batch_size=4,\n",
        "    validation_split=validation_split,\n",
        "    shuffle=True,\n",
        "    verbose=1,\n",
        ")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"ANN-3x64\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_4 (Dense)              (None, 64)                2752      \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 27)                1755      \n",
            "=================================================================\n",
            "Total params: 12,827\n",
            "Trainable params: 12,827\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.8565 - accuracy: 0.7725 - val_loss: 0.0948 - val_accuracy: 0.9839\n",
            "Epoch 2/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0676 - accuracy: 0.9822 - val_loss: 0.0447 - val_accuracy: 0.9943\n",
            "Epoch 3/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0244 - accuracy: 0.9934 - val_loss: 0.0177 - val_accuracy: 0.9977\n",
            "Epoch 4/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0165 - accuracy: 0.9957 - val_loss: 0.0071 - val_accuracy: 0.9989\n",
            "Epoch 5/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0173 - accuracy: 0.9951 - val_loss: 0.0208 - val_accuracy: 0.9920\n",
            "Epoch 6/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0111 - accuracy: 0.9965 - val_loss: 0.0039 - val_accuracy: 0.9989\n",
            "Epoch 7/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0026 - accuracy: 0.9997 - val_loss: 0.0020 - val_accuracy: 0.9989\n",
            "Epoch 8/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0314 - accuracy: 0.9919 - val_loss: 0.0026 - val_accuracy: 1.0000\n",
            "Epoch 9/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0037 - accuracy: 0.9991 - val_loss: 5.9867e-04 - val_accuracy: 1.0000\n",
            "Epoch 10/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0084 - accuracy: 0.9983 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
            "Epoch 11/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0074 - accuracy: 0.9980 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
            "Epoch 12/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0030 - accuracy: 0.9988 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "Epoch 13/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0148 - accuracy: 0.9948 - val_loss: 8.3820e-04 - val_accuracy: 1.0000\n",
            "Epoch 14/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0020 - accuracy: 0.9991 - val_loss: 2.7807e-04 - val_accuracy: 1.0000\n",
            "Epoch 15/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0052 - accuracy: 0.9986 - val_loss: 3.6428e-04 - val_accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUaetVfOae-Q",
        "outputId": "dce75f65-d00f-4acb-d3e7-ee05ea63d0cb"
      },
      "source": [
        "model = keras.models.Sequential(name = 'ANN-3x64-Dropouts',\n",
        "                                   layers =\n",
        "    [\n",
        "        keras.layers.InputLayer(input_shape=input_dim),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dropout(0.3),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dropout(0.3),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dropout(0.3),\n",
        "        keras.layers.Dense(len(labels), activation=keras.activations.softmax),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(),\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "model_train_history[model] = model.fit(\n",
        "    x=x_train,\n",
        "    y=y_train,\n",
        "    epochs=epochs,\n",
        "    batch_size=4,\n",
        "    validation_split=validation_split,\n",
        "    shuffle=True,\n",
        "    verbose=1,\n",
        ")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"ANN-3x64-Dropouts\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_8 (Dense)              (None, 64)                2752      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 27)                1755      \n",
            "=================================================================\n",
            "Total params: 12,827\n",
            "Trainable params: 12,827\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 1.9308 - accuracy: 0.3897 - val_loss: 0.4994 - val_accuracy: 0.8966\n",
            "Epoch 2/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.7115 - accuracy: 0.7449 - val_loss: 0.1693 - val_accuracy: 0.9678\n",
            "Epoch 3/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.4514 - accuracy: 0.8435 - val_loss: 0.0786 - val_accuracy: 0.9908\n",
            "Epoch 4/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.3256 - accuracy: 0.8944 - val_loss: 0.0548 - val_accuracy: 0.9931\n",
            "Epoch 5/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.2681 - accuracy: 0.9108 - val_loss: 0.0292 - val_accuracy: 0.9977\n",
            "Epoch 6/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.2054 - accuracy: 0.9373 - val_loss: 0.0237 - val_accuracy: 0.9966\n",
            "Epoch 7/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.1761 - accuracy: 0.9379 - val_loss: 0.0170 - val_accuracy: 0.9966\n",
            "Epoch 8/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.1785 - accuracy: 0.9405 - val_loss: 0.0127 - val_accuracy: 0.9977\n",
            "Epoch 9/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.1387 - accuracy: 0.9557 - val_loss: 0.0084 - val_accuracy: 0.9989\n",
            "Epoch 10/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.1311 - accuracy: 0.9571 - val_loss: 0.0070 - val_accuracy: 0.9989\n",
            "Epoch 11/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.1086 - accuracy: 0.9655 - val_loss: 0.0082 - val_accuracy: 0.9966\n",
            "Epoch 12/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.1182 - accuracy: 0.9612 - val_loss: 0.0046 - val_accuracy: 0.9989\n",
            "Epoch 13/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.1024 - accuracy: 0.9664 - val_loss: 0.0030 - val_accuracy: 1.0000\n",
            "Epoch 14/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.1046 - accuracy: 0.9666 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
            "Epoch 15/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0746 - accuracy: 0.9773 - val_loss: 0.0025 - val_accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FA1LSFoVae-R",
        "outputId": "0765de87-c32f-4080-bc57-bebec642df6b"
      },
      "source": [
        "model = keras.models.Sequential(name = 'ANN-2x128',\n",
        "                                   layers =\n",
        "    [\n",
        "        keras.layers.InputLayer(input_shape=input_dim),\n",
        "        keras.layers.Dense(128, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(128, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(len(labels), activation=keras.activations.softmax),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(),\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "model_train_history[model] = model.fit(\n",
        "    x=x_train,\n",
        "    y=y_train,\n",
        "    epochs=epochs,\n",
        "    batch_size=4,\n",
        "    validation_split=validation_split,\n",
        "    shuffle=True,\n",
        "    verbose=1,\n",
        ")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"ANN-2x128\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_12 (Dense)             (None, 128)               5504      \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 27)                3483      \n",
            "=================================================================\n",
            "Total params: 25,499\n",
            "Trainable params: 25,499\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.6936 - accuracy: 0.8519 - val_loss: 0.0787 - val_accuracy: 0.9770\n",
            "Epoch 2/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0510 - accuracy: 0.9853 - val_loss: 0.0244 - val_accuracy: 0.9943\n",
            "Epoch 3/15\n",
            "870/870 [==============================] - 3s 3ms/step - loss: 0.0206 - accuracy: 0.9945 - val_loss: 0.0077 - val_accuracy: 0.9989\n",
            "Epoch 4/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0119 - accuracy: 0.9971 - val_loss: 0.0123 - val_accuracy: 0.9989\n",
            "Epoch 5/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0112 - accuracy: 0.9977 - val_loss: 0.0040 - val_accuracy: 1.0000\n",
            "Epoch 6/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.0010 - val_accuracy: 1.0000\n",
            "Epoch 7/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0141 - accuracy: 0.9983 - val_loss: 0.0042 - val_accuracy: 1.0000\n",
            "Epoch 8/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0075 - accuracy: 0.9986 - val_loss: 8.5079e-04 - val_accuracy: 1.0000\n",
            "Epoch 9/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0066 - accuracy: 0.9977 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "Epoch 10/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 3.2381e-04 - val_accuracy: 1.0000\n",
            "Epoch 11/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 0.0013 - val_accuracy: 0.9989\n",
            "Epoch 12/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0058 - accuracy: 0.9991 - val_loss: 6.3880e-04 - val_accuracy: 1.0000\n",
            "Epoch 13/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 5.1653e-04 - val_accuracy: 1.0000\n",
            "Epoch 14/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0078 - accuracy: 0.9980 - val_loss: 2.8969e-04 - val_accuracy: 1.0000\n",
            "Epoch 15/15\n",
            "870/870 [==============================] - 2s 3ms/step - loss: 0.0040 - accuracy: 0.9994 - val_loss: 3.1832e-04 - val_accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "4UhLcq1Jae-R",
        "outputId": "abf3599b-bf59-491e-bfc4-515502db0c72"
      },
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(14,5))\n",
        "colors_graph = [\"#d69e2d\",\n",
        "                \"#927ced\",\n",
        "                \"#73bd4d\",\n",
        "                \"#e462c0\",\n",
        "                \"#eb5e52\"]\n",
        "handles = []\n",
        "\n",
        "for (model, history), color in zip(model_train_history.items(), colors_graph):\n",
        "    label = '{} ({})'.format(model.name, model.count_params())\n",
        "    axs[0].plot(history.history['loss'], c=color, ls='-.', alpha=.7)\n",
        "    axs[1].plot(history.history['accuracy'], c=color, ls='-.', alpha=.7)\n",
        "    axs[0].plot(history.history['val_loss'], c=color)\n",
        "    axs[1].plot(history.history['val_accuracy'], c=color)\n",
        "    handles.append(mpatches.Patch(color=color, label=label))\n",
        "\n",
        "for ax in axs:\n",
        "    ax.set_xlabel('Epoch')\n",
        "axs[0].set_ylabel('loss')\n",
        "axs[0].set_yscale('log')\n",
        "axs[1].set_ylabel('accuracy')\n",
        "axs[1].set_ylim(0.95,1.01)\n",
        "\n",
        "handles.append(Line2D([0], [0], color='grey', lw=1, ls='-', label='validation'))\n",
        "handles.append(Line2D([0], [0], color='grey', lw=1, ls='-.', label='training'))\n",
        "\n",
        "fig.subplots_adjust(right=0.85)\n",
        "fig.legend(handles=handles,\n",
        "           loc=\"center right\",\n",
        "           borderaxespad=1)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb4b8566550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmJ_LECNae-R"
      },
      "source": [
        "## Model export"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "km8bpVuSrwPE"
      },
      "source": [
        "Once you have a good model, you can save it on your Google Drive. The model is saved using the [folder hierarchy of OpenHand](https://github.com/ArthurFDLR/OpenHand-App#pose-classifier-models)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyD7zsZSfuMk",
        "outputId": "7540da22-fb4f-4c1e-9362-44bc8fd79fea"
      },
      "source": [
        "from pathlib import Path\n",
        "import json\n",
        "\n",
        "model_name = 'ANN_RightHand_1'\n",
        "\n",
        "if IN_COLAB:\n",
        "    content_path = Path('/').absolute() / 'content'\n",
        "    drive_path = content_path / 'drive'\n",
        "    google.colab.drive.mount(str(drive_path))\n",
        "    save_path = drive_path / 'My Drive'\n",
        "    \n",
        "    for subfolder in ['Pose Classification Kit', 'Models', model_name]:\n",
        "        save_path /= subfolder\n",
        "        if not (save_path).is_dir():\n",
        "            %mkdir \"{save_path}\"\n",
        "else:\n",
        "    save_path = Path('.').absolute() / model_name\n",
        "    %mkdir \"{save_path}\"\n",
        "\n",
        "model_path = save_path / '{name}.h5'.format(name = model_name)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pc5o55gae-R",
        "outputId": "3299aa0d-d9e6-4d93-bcb8-ad6745c56601"
      },
      "source": [
        "model = keras.models.Sequential(name = '27Class_3x64',\n",
        "                                   layers =\n",
        "    [\n",
        "        keras.layers.InputLayer(input_shape=input_dim),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(64, activation=keras.activations.relu),\n",
        "        keras.layers.Dense(len(labels), activation=keras.activations.softmax),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(),\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "model.fit(\n",
        "    x=x_train,\n",
        "    y=y_train,\n",
        "    epochs=10,\n",
        "    batch_size=4,\n",
        "    validation_split=0.15,\n",
        "    shuffle=True,\n",
        "    callbacks=[keras.callbacks.ModelCheckpoint(filepath=model_path, verbose=1, save_best_only=True)],\n",
        "    verbose = 2,\n",
        ")\n",
        "\n",
        "with open(save_path / 'class.json', 'w') as f:\n",
        "    json.dump({'labels':labels}, f)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"27Class_3x64\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_15 (Dense)             (None, 64)                2752      \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 27)                1755      \n",
            "=================================================================\n",
            "Total params: 12,827\n",
            "Trainable params: 12,827\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/10\n",
            "924/924 - 3s - loss: 0.8493 - accuracy: 0.7745 - val_loss: 0.1433 - val_accuracy: 0.9740\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.14327, saving model to /content/drive/My Drive/Pose Classification Kit/Models/ANN_RightHand_1/ANN_RightHand_1.h5\n",
            "Epoch 2/10\n",
            "924/924 - 2s - loss: 0.0822 - accuracy: 0.9764 - val_loss: 0.0242 - val_accuracy: 0.9969\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.14327 to 0.02420, saving model to /content/drive/My Drive/Pose Classification Kit/Models/ANN_RightHand_1/ANN_RightHand_1.h5\n",
            "Epoch 3/10\n",
            "924/924 - 2s - loss: 0.0312 - accuracy: 0.9919 - val_loss: 0.0103 - val_accuracy: 0.9985\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.02420 to 0.01033, saving model to /content/drive/My Drive/Pose Classification Kit/Models/ANN_RightHand_1/ANN_RightHand_1.h5\n",
            "Epoch 4/10\n",
            "924/924 - 2s - loss: 0.0204 - accuracy: 0.9940 - val_loss: 0.0071 - val_accuracy: 0.9985\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.01033 to 0.00710, saving model to /content/drive/My Drive/Pose Classification Kit/Models/ANN_RightHand_1/ANN_RightHand_1.h5\n",
            "Epoch 5/10\n",
            "924/924 - 2s - loss: 0.0323 - accuracy: 0.9919 - val_loss: 0.0274 - val_accuracy: 0.9893\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.00710\n",
            "Epoch 6/10\n",
            "924/924 - 2s - loss: 0.0235 - accuracy: 0.9935 - val_loss: 0.0069 - val_accuracy: 0.9985\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.00710 to 0.00693, saving model to /content/drive/My Drive/Pose Classification Kit/Models/ANN_RightHand_1/ANN_RightHand_1.h5\n",
            "Epoch 7/10\n",
            "924/924 - 2s - loss: 0.0083 - accuracy: 0.9978 - val_loss: 0.0370 - val_accuracy: 0.9847\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.00693\n",
            "Epoch 8/10\n",
            "924/924 - 2s - loss: 0.0174 - accuracy: 0.9951 - val_loss: 0.0052 - val_accuracy: 0.9969\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.00693 to 0.00520, saving model to /content/drive/My Drive/Pose Classification Kit/Models/ANN_RightHand_1/ANN_RightHand_1.h5\n",
            "Epoch 9/10\n",
            "924/924 - 2s - loss: 0.0086 - accuracy: 0.9981 - val_loss: 0.0019 - val_accuracy: 1.0000\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.00520 to 0.00187, saving model to /content/drive/My Drive/Pose Classification Kit/Models/ANN_RightHand_1/ANN_RightHand_1.h5\n",
            "Epoch 10/10\n",
            "924/924 - 2s - loss: 0.0178 - accuracy: 0.9943 - val_loss: 0.0372 - val_accuracy: 0.9847\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.00187\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPMnJf1Vae-S",
        "outputId": "d0a29d6d-64ea-49db-b695-3ad94f1d003e"
      },
      "source": [
        "model = keras.models.load_model(model_path)\n",
        "model.evaluate(x=dataset['x_test'], y=dataset['y_test_onehot'])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "34/34 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9991\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0032896932680159807, 0.9990741014480591]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3lsa7aKae-S",
        "outputId": "6cee19e7-4221-44fd-b16f-b1a2fe951887",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import itertools\n",
        "labels_predict = model.predict(dataset['x_test'])\n",
        "confusion_matrix = np.array(\n",
        "    tensorflow.math.confusion_matrix(\n",
        "        np.argmax(labels_predict,axis=1),\n",
        "        np.argmax(dataset['y_test_onehot'],axis=1)\n",
        "))\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,10), dpi=100)\n",
        "\n",
        "ax.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "tick_marks = np.arange(len(dataset['labels']))\n",
        "ax.set_xticks(tick_marks)\n",
        "ax.set_xticklabels(dataset['labels'], rotation=40, ha='right')\n",
        "ax.set_yticks(tick_marks)\n",
        "ax.set_yticklabels(dataset['labels'])\n",
        "\n",
        "thresh = np.max(confusion_matrix) / 2.\n",
        "for i, j in itertools.product(range(confusion_matrix.shape[0]), range(confusion_matrix.shape[1])):\n",
        "    plt.text(j, i, confusion_matrix[i, j],\n",
        "        horizontalalignment=\"center\",\n",
        "        color=\"white\" if confusion_matrix[i, j] > thresh else \"black\")\n",
        "\n",
        "fig.tight_layout()\n",
        "ax.set_ylabel('True label', fontsize=12)\n",
        "ax.set_xlabel('Predicted label', fontsize=12)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 36.4591284279378, 'Predicted label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}